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Dr. Viviana Maggioni leads an active and interdisciplinary team of postdoctoral reasearch fellows, graduate and undergraduate students at George Mason University. Our research activities span from the local scale, by monitoring and modeling stormwater quantity and quality at the Mason main campus with state-of-the-art sensor networks, to the global scale, combining water resources engineering with hydrometeorology and remote sensing using satellite data to evaluate conditions in remote regions, where ground truthing is impossible, but where environmental and health consequences can be devastating.
Freeze/thaw (FT) processes at the earth's surface can have a considerable effect on global carbon, energy, and hydrologic cycles. Therefore, an accurate representation of FT is valuable to adequately monitor and model these processes. In this study, we assess the relationship between satellite-based FT products and modeled surface and soil temperatures over North America. In addition, hourly land surface temperature (LST) from the Geostationary Operational Environmental Satellite (GOES) system is also compared to FT classifications. Utilizing the higher spatial resolution temperatures (~5 km), we assess subgrid-scale variability and its relationship to coarser microwave FT classifications (>25 km). We also examine product agreement and subpixel characteristics across the land cover, climate, and topography. FT classifications are shown to vary widely depending on these variables, leading to an ambiguous definition of frozen and thawed states. Our results suggest that current products can characterize FT transitions with consistent subfreezing surface characteristics in far northern regions (>50 °N). However, uncertainty associated with FT classifications is shown to increase considerably as latitude decreases. Our results also suggest that fractional FT products, utilizing data inputs, such as LST, would provide a considerable improvement in mountainous regions with high intergrid cell heterogeneity, in regions characterized by ephemeral FT events (i.e., regions < 40 °N), as well as during freeze and thaw onset periods. This study also provides insight to improving the representation of surface FT state by providing a clearer definition of the subpixel scale temperature characteristics that govern existing frozen classifications.
Jeremy M. Johnston; Paul R. Houser; Viviana Maggioni; Rhae Sung Kim; Carrie Vuyovich. Informing Improvements in Freeze/Thaw State Classification Using Subpixel Temperature. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -19.
AMA StyleJeremy M. Johnston, Paul R. Houser, Viviana Maggioni, Rhae Sung Kim, Carrie Vuyovich. Informing Improvements in Freeze/Thaw State Classification Using Subpixel Temperature. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-19.
Chicago/Turabian StyleJeremy M. Johnston; Paul R. Houser; Viviana Maggioni; Rhae Sung Kim; Carrie Vuyovich. 2021. "Informing Improvements in Freeze/Thaw State Classification Using Subpixel Temperature." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-19.
The usefulness of satellite multisensor precipitation products such as NASA’s 30-minute, 0.1° Integrated Multi-satellitE Retrievals for the Global Precipitation Mission (IMERG) is hindered by their associated errors. Reliable estimates of uncertainty would mitigate this limitation, especially in near-real time. Creating such estimates is challenging, however, due both to the complex discrete-continuous nature of satellite precipitation errors and to the lack of “ground truth” data precisely in the places—including complex terrain and developing countries—that could benefit most from satellite precipitation estimates. In this work, we use swath-based precipitation products from the Global Precipitation Mission (GPM) Dual-frequency Precipitation Radar (DPR) as an alternative to ground-based observations to facilitate IMERG uncertainty estimation. We compare the suitability of two DPR derived products, 2ADPR and 2BCMB, against higher-fidelity Ground Validation Multi-Radar Multi-Sensor (GV-MRMS) ground reference data over the contiguous United States. 2BCMB is selected to train mixed discrete-continuous error models based on Censored Shifted Gamma Distributions. Uncertainty estimates from these error models are compared against alternative models trained on GV-MRMS. Using information from NASA’s Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis, we also demonstrate how IMERG uncertainty estimates can be further constrained using additional precipitation-related predictors. Though several critical issues remain unresolved, the proposed method shows promise for yielding robust uncertainty estimates in near-real time for IMERG and other similar precipitation products at their native resolution across the entire globe.
Zhe LiiD; Daniel WrightiD; Samantha HartkeiD; Dalia Kirschbaum; Sana Khan; Viviana MaggioniiD; Pierre-Emmanuel KirstetteriD. Toward A Globally-Applicable Uncertainty Quantification Framework for Satellite Multisensor Precipitation Products based on GPM DPR. 2021, 1 .
AMA StyleZhe LiiD, Daniel WrightiD, Samantha HartkeiD, Dalia Kirschbaum, Sana Khan, Viviana MaggioniiD, Pierre-Emmanuel KirstetteriD. Toward A Globally-Applicable Uncertainty Quantification Framework for Satellite Multisensor Precipitation Products based on GPM DPR. . 2021; ():1.
Chicago/Turabian StyleZhe LiiD; Daniel WrightiD; Samantha HartkeiD; Dalia Kirschbaum; Sana Khan; Viviana MaggioniiD; Pierre-Emmanuel KirstetteriD. 2021. "Toward A Globally-Applicable Uncertainty Quantification Framework for Satellite Multisensor Precipitation Products based on GPM DPR." , no. : 1.
This first paper of the two‐part series focuses on demonstrating the accuracy of a hyper‐resolution, offline terrestrial modeling system used for the High Mountain Asia (HMA) region. To this end, this study systematically evaluates four sets of model simulations at point scale, basin scale, and domain scale obtained from different spatial resolutions including 0.01° ( ∼ 1‐km) and 0.25° ( ∼ 25‐km). The assessment is conducted via comparisons against ground‐based observations and satellite‐derived reference products. The key variables of interest include surface net shortwave radiation, surface net longwave radiation, skin temperature, near‐surface soil temperature, snow depth, snow water equivalent, and total runoff. In the evaluation against ground‐based measurements, the superiority of the 0.01° estimates are mostly demonstrated across relatively complex terrain. Specifically, hyper‐resolution modeling improves the skill in meteorological forcing estimates (except precipitation) by 9% relative to coarse‐resolution estimates. The model forced by downscaled forcings in its entirety yields the highest skill in model output states as well as precipitation, which improves the skill obtained by coarse‐resolution estimates by 7%. These findings, on one hand, corroborate the importance of employing the hyper‐resolution versus coarse‐resolution modeling in areas characterized by complex terrain. On the other hand, by evaluating four sets of model simulations forced with different precipitation products, this study emphasizes the importance of accurate hyper‐resolution precipitation products to drive model simulations.
Yuan Xue; Paul R. Houser; Viviana Maggioni; Yiwen Mei; Sujay V. Kumar; Yeosang Yoon. Evaluation of High Mountain Asia‐Land Data Assimilation System (Version 1) From 2003 to 2016, Part I: A Hyper‐Resolution Terrestrial Modeling System. Journal of Geophysical Research: Atmospheres 2021, 126, 1 .
AMA StyleYuan Xue, Paul R. Houser, Viviana Maggioni, Yiwen Mei, Sujay V. Kumar, Yeosang Yoon. Evaluation of High Mountain Asia‐Land Data Assimilation System (Version 1) From 2003 to 2016, Part I: A Hyper‐Resolution Terrestrial Modeling System. Journal of Geophysical Research: Atmospheres. 2021; 126 (8):1.
Chicago/Turabian StyleYuan Xue; Paul R. Houser; Viviana Maggioni; Yiwen Mei; Sujay V. Kumar; Yeosang Yoon. 2021. "Evaluation of High Mountain Asia‐Land Data Assimilation System (Version 1) From 2003 to 2016, Part I: A Hyper‐Resolution Terrestrial Modeling System." Journal of Geophysical Research: Atmospheres 126, no. 8: 1.
The Gravity Recovery and Climate Experiment (GRACE) mission and its Follow-On (GRACE-FO) mission provide unprecedented observations of terrestrial water storage (TWS) dynamics at basin to continental scales. Established GRACE data assimilation techniques directly adjust the simulated water storage components to improve the estimation of groundwater, streamflow, and snow water equivalent. Such techniques artificially add/subtract water to/from prognostic variables, thus upsetting the simulated water balance. To overcome this limitation, we propose and test an alternative assimilation scheme in which precipitation fluxes are adjusted to achieve the desired changes in simulated TWS. Using a synthetic data assimilation experiment, we show that the scheme improves performance skill in precipitation estimates in general, but that it is more robust for snowfall than for rainfall, and it fails in certain regions with strong horizontal gradients in precipitation. The results demonstrate that assimilation of TWS observations can help correct (adjust) the model’s precipitation forcing and, in turn, enhance model estimates of TWS, snow mass, soil moisture, runoff, and evaporation. A key limitation of the approach is the assumption that all errors in TWS originate from errors in precipitation. Nevertheless, the proposed approach produces more consistent improvements in simulated runoff than the established GRACE data assimilation techniques.
Manuela Girotto; Rolf Reichle; Matthew Rodell; Viviana Maggioni. Data Assimilation of Terrestrial Water Storage Observations to Estimate Precipitation Fluxes: A Synthetic Experiment. Remote Sensing 2021, 13, 1223 .
AMA StyleManuela Girotto, Rolf Reichle, Matthew Rodell, Viviana Maggioni. Data Assimilation of Terrestrial Water Storage Observations to Estimate Precipitation Fluxes: A Synthetic Experiment. Remote Sensing. 2021; 13 (6):1223.
Chicago/Turabian StyleManuela Girotto; Rolf Reichle; Matthew Rodell; Viviana Maggioni. 2021. "Data Assimilation of Terrestrial Water Storage Observations to Estimate Precipitation Fluxes: A Synthetic Experiment." Remote Sensing 13, no. 6: 1223.
The potential of global high-resolution near-realtime multi-sensor merged satellite precipitation products such as NASA’s 30-minute, 0.1° Integrated Multi-satellitE Retrievals for Global Precipitation Mission (IMERG) to monitor, characterize and model the water cycle has been widely recognized. Despite continuing improvements in the coverage, accuracy, and resolution of these products, their usefulness in real-world applications is still limited by the lack of insight into errors in estimated precipitation and the ability to properly quantify errors in ways that benefit various end users. A fundamental limitation is the lack of reliable “ground truth” data (e.g., rain gauges or ground weather radars)—such reference observations are lacking in precisely the places (complex terrain, ungauged areas, and developing countries) that could benefit most from satellite products. Moreover, error characterization of satellite precipitation products poses a unique challenge due to the “mixed” discrete and continuous distribution of errors, a challenge that is increasingly important to address as satellite precipitation products advance to higher resolutions.
In this work, we propose to use the instantaneous swath-based data products from the Dual-frequency Precipitation Radar (DPR) aboard the GPM core observatory as an alternative reference to replace ground observations—which could facilitate IMERG global error estimation at its native resolution. We compare two DPR-based products, 2ADPR and 2BCMB, against the Multi-Radar/Multi-Sensor (MRMS) data over the contiguous United States (CONUS). We then select 2BCMB to train a mixed discrete-continuous error model based on the Censored Shifted Gamma Distribution (CSGD) to estimate IMERG errors. This error model is evaluated and compared against an alternative CSGD model trained on MRMS data in the CONUS during 2014-2019. Using NASA’s MERRA-2 reanalysis products, we also demonstrate how IMERG errors can be further constrained by including ancillary information as covariates within the error model. This error modeling framework will be further examined at several ground validation sites around the globe (e.g., WegenerNet, AMMA-CATCH among others) to evaluate its robustness under different climatic, land cover, and DPR sampling conditions.
Zhe Li; Daniel Wright; Samantha Hartke; Dalia Kirschbaum; Sana Khan; Viviana Maggioni; Pierre-Emmanuel Kirstetter. A Prototype IMERG Error Modeling Framework based on GPM DPR Observations and its Global Validation. 2021, 1 .
AMA StyleZhe Li, Daniel Wright, Samantha Hartke, Dalia Kirschbaum, Sana Khan, Viviana Maggioni, Pierre-Emmanuel Kirstetter. A Prototype IMERG Error Modeling Framework based on GPM DPR Observations and its Global Validation. . 2021; ():1.
Chicago/Turabian StyleZhe Li; Daniel Wright; Samantha Hartke; Dalia Kirschbaum; Sana Khan; Viviana Maggioni; Pierre-Emmanuel Kirstetter. 2021. "A Prototype IMERG Error Modeling Framework based on GPM DPR Observations and its Global Validation." , no. : 1.
This work investigates the inter-relationships among stream water quality indicators, hydroclimatic variables (e.g., precipitation, river discharge), and land characteristics (e.g., soil type, land use), which is crucial to developing effective methods for water quality protection. The potential of using statistical tools, such as Principal Component (PC) and Granger causality analyses, for this purpose is assessed across 10 watersheds in the Eastern United States. The PC analysis shows consistency across the ten locations, with most of the variation explained by the first two PCs, except for the least developed watershed that presents three PCs. Results show that stronger Granger causality relationships and correlation coefficients are identified when considering a lag of one day, compared to longer lags. This is mainly due to the watersheds’ limited size and, thus, their fast hydrological response. The strongest Granger causalities are observed when water temperature and dissolved oxygen concentration are considered as the effect of the other variables, which corroborates the importance of these two water properties. This work also demonstrates how watershed size and land use can impact causalities between hydrometeorological variables and water quality, thus, highlighting how complex these relationships are even in a region characterized by overall similar climatology.
Maryam Zavareh; Viviana Maggioni; Vadim Sokolov. Investigating Water Quality Data Using Principal Component Analysis and Granger Causality. Water 2021, 13, 343 .
AMA StyleMaryam Zavareh, Viviana Maggioni, Vadim Sokolov. Investigating Water Quality Data Using Principal Component Analysis and Granger Causality. Water. 2021; 13 (3):343.
Chicago/Turabian StyleMaryam Zavareh; Viviana Maggioni; Vadim Sokolov. 2021. "Investigating Water Quality Data Using Principal Component Analysis and Granger Causality." Water 13, no. 3: 343.
Existing global FT records, derived from the Soil Moisture Active Passive (SMAP), the Advanced Scanning Microwave Radiometer (AMSR), and the Special Sensor Microwave Imager (SSM/I) produce relatively course spatial resolution (25-36km) binary FT classifications. These classifications can vary widely depending on the microwave bands used, topography, and land cover, leading to a somewhat ambiguous definition of ‘frozen’ and ‘thawed’ states. In this study, we assess the relationship between satellite observation derived FT products over North America compared to modeled near-surface temperatures and land surface temperature (LST) from the Geostationary Operational Environmental Satellite system (GOES). Utilizing the higher spatial resolution of these products (~4.5km), sub-grid scale variability and its relationship to courser microwave FT classifications was assessed. Through an analysis of spatial variability and uncertainty across North America, five focus study pixels each representing unique FT profiles were examined. These included pixels in: (1) Southern Plains (36, -97), (2) Tundra (61, -76), (3) Northern Forest (47, -74), (4) Northern Plains (52, -103), and Mountainous (38.9, -107.9). The model ensemble adequately captured near surface temperatures as they relate to FT classifications in Tundra, the Northern Plains, and Northern Forest regions. On average, 85.3% to 99.6% of sub-grid cells were below freezing when FT products classified the associated pixels as frozen. GOES - LST observations were shown to have the highest proportion of sub-grid cells below freezing on average, when classified as frozen by FT products (97.3% - 100%) across the same 3 focus locations. However, we also find that fractional FT products utilizing higher resolution data inputs, such as LST, would provide a considerable improvement in mountainous regions with high inter-grid cell heterogeneity, in regions characterized by ephemeral FT events (Southern Plains), as well as during freeze and thaw onset periods. These locations showed a significant reduction in the average temperature product frozen proportion associated with frozen classifications (as low as 5.8%). This study provides insight to improving representation of FT state and providing a clearer meaning of what constitutes a ‘frozen’ classification.
Jeremy JohnstoniD; Paul Houser; Viviana MaggioniiD. Informing Improvements in Microwave Freeze/Thaw Products using High-Resolution Temperature Data Over North America. 2020, 1 .
AMA StyleJeremy JohnstoniD, Paul Houser, Viviana MaggioniiD. Informing Improvements in Microwave Freeze/Thaw Products using High-Resolution Temperature Data Over North America. . 2020; ():1.
Chicago/Turabian StyleJeremy JohnstoniD; Paul Houser; Viviana MaggioniiD. 2020. "Informing Improvements in Microwave Freeze/Thaw Products using High-Resolution Temperature Data Over North America." , no. : 1.
The accurate representation of the local‐scale variability of precipitation plays an important role in understanding the hydrological cycle and land‐atmosphere interactions in the High Mountain Asia region. Therefore, the development of hyper‐resolution precipitation data is of urgent need. In this study, we propose a statistical framework to downscale the Modern‐Era Retrospective analysis for Research and Applications, version 2 (MERRA‐2) precipitation product using the random forest classification and regression algorithm. A set of variables representing atmospheric, geographic, and vegetation cover information are selected as model predictors, based on a recursive feature elimination method. The downscaled precipitation product is validated in terms of magnitude and variability against a set of ground‐ and satellite‐based observations. Results suggest improvements with respect to the original resolution MERRA‐2 precipitation product and comparable performance with gauge‐adjusted satellite precipitation products.
Yiwen Mei; Viviana Maggioni; Paul Houser; Yuan Xue; Tasnuva Rouf. A Nonparametric Statistical Technique for Spatial Downscaling of Precipitation Over High Mountain Asia. Water Resources Research 2020, 56, 1 .
AMA StyleYiwen Mei, Viviana Maggioni, Paul Houser, Yuan Xue, Tasnuva Rouf. A Nonparametric Statistical Technique for Spatial Downscaling of Precipitation Over High Mountain Asia. Water Resources Research. 2020; 56 (11):1.
Chicago/Turabian StyleYiwen Mei; Viviana Maggioni; Paul Houser; Yuan Xue; Tasnuva Rouf. 2020. "A Nonparametric Statistical Technique for Spatial Downscaling of Precipitation Over High Mountain Asia." Water Resources Research 56, no. 11: 1.
This study investigated the propagation of errors in input satellite-based precipitation products (SPPs) on streamflow and water quality indicators simulated by a hydrological model in the Occoquan Watershed, located in the suburban Washington, D.C. area. A dense rain gauge network was used as reference to evaluate three SPPs which are based on different retrieval algorithms. A Hydrologic Simulation Program-FORTRAN (HSPF) hydrology and water quality model was forced with the three SPPs to simulate output of streamflow (Q), total suspended solids (TSS), stream temperature (TW), and dissolved oxygen (DO). Results indicate that the HSPF model may have a dampening effect on the precipitation-to-streamflow error. The bias error propagation of all three SPPs showed a positive dependency on basin scale for streamflow and TSS, but not for TW and DO. On a seasonal basis, bias error propagation varied by product, with larger values generally found in fall and winter. This study demonstrated that the spatiotemporal variability of SPPs, along with their algorithms to estimate precipitation, have an influence on water quality simulations in a hydrologic model.
Jennifer Solakian; Viviana Maggioni; Adil Godrej. Investigating the Error Propagation from Satellite-Based Input Precipitation to Output Water Quality Indicators Simulated by a Hydrologic Model. Remote Sensing 2020, 12, 3728 .
AMA StyleJennifer Solakian, Viviana Maggioni, Adil Godrej. Investigating the Error Propagation from Satellite-Based Input Precipitation to Output Water Quality Indicators Simulated by a Hydrologic Model. Remote Sensing. 2020; 12 (22):3728.
Chicago/Turabian StyleJennifer Solakian; Viviana Maggioni; Adil Godrej. 2020. "Investigating the Error Propagation from Satellite-Based Input Precipitation to Output Water Quality Indicators Simulated by a Hydrologic Model." Remote Sensing 12, no. 22: 3728.
This article presents an online teaching tool that introduces students to basic concepts of remote sensing and its applications in hydrology. The learning module is intended for junior/senior undergraduate students or junior graduate students with no (or little) prior experience in remote sensing, but with some basic background of environmental science, hydrology, statistics, and programming. This e-learning environment offers background content on the fundamentals of remote sensing, but also integrates a set of existing online tools for visualization and analysis of satellite observations. Specifically, students are introduced to a variety of satellite products and techniques that can be used to monitor and analyze changes in the hydrological cycle. At completion of the module, students are able to visualize remote sensing data (both in terms of time series and spatial maps), detect temporal trends, interpret satellite images, and assess errors and uncertainties in a remote sensing product. Students are given the opportunity to check their understanding as they progress through the module and also tackle complex real-life problems using remote sensing observations that professionals and scientists commonly use in practice. The learning tool is implemented in HydroLearn, an open-source, online platform for instructors to find and share learning modules and collaborate on developing teaching resources in hydrology and water resources.
Viviana Maggioni; Manuela Girotto; Emad Habib; Melissa Gallagher. Building an Online Learning Module for Satellite Remote Sensing Applications in Hydrologic Science. Remote Sensing 2020, 12, 3009 .
AMA StyleViviana Maggioni, Manuela Girotto, Emad Habib, Melissa Gallagher. Building an Online Learning Module for Satellite Remote Sensing Applications in Hydrologic Science. Remote Sensing. 2020; 12 (18):3009.
Chicago/Turabian StyleViviana Maggioni; Manuela Girotto; Emad Habib; Melissa Gallagher. 2020. "Building an Online Learning Module for Satellite Remote Sensing Applications in Hydrologic Science." Remote Sensing 12, no. 18: 3009.
This study evaluates the potential of assimilating phenology observations using a direct insertion (DI) method by constraining the modeled terrestrial carbon dynamics with synthetic observations of vegetation condition. Specifically, observations of leaf area index (LAI) are assimilated in the Noah-Multi Parameterization (Noah-MP) land surface model across the continental United States during a 5-year period. An observing system simulation experiment (OSSE) was developed to understand and quantify the model response to assimilating LAI information through DI when the input precipitation is strongly biased. This is particularly significant in data poor regions, like Africa and South Asia, where satellite and re-analysis products, known to be affected by significant biases, are the only available precipitation data to drive a land surface model. Results show a degradation in surface and rootzone soil moisture after assimilating LAI within Noah-MP, but an improvement in intercepted liquid water and evapotranspiration with respect to the open-loop simulation (a free run with no LAI assimilation). In terms of carbon and energy variables, net ecosystem exchange, amount of carbon in shallow soil, and surface soil temperature are improved by the LAI DI, although canopy sensible heat is degraded. Overall, the assimilation of LAI has larger impact in terms of reduced systematic and random errors over the Great Plains (cropland, shrubland, and grassland). Moreover, LAI DA shows a greater improvement when the input precipitation is affected by a positive (wet) bias than the opposite case, in which precipitation shows a dry bias.
Azbina Rahman; Xinxuan Zhang; Yuan Xue; Paul Houser; Timothy Sauer; Sujay Kumar; David Mocko; Viviana Maggioni. A synthetic experiment to investigate the potential of assimilating LAI through direct insertion in a land surface model. Journal of Hydrology X 2020, 9, 100063 .
AMA StyleAzbina Rahman, Xinxuan Zhang, Yuan Xue, Paul Houser, Timothy Sauer, Sujay Kumar, David Mocko, Viviana Maggioni. A synthetic experiment to investigate the potential of assimilating LAI through direct insertion in a land surface model. Journal of Hydrology X. 2020; 9 ():100063.
Chicago/Turabian StyleAzbina Rahman; Xinxuan Zhang; Yuan Xue; Paul Houser; Timothy Sauer; Sujay Kumar; David Mocko; Viviana Maggioni. 2020. "A synthetic experiment to investigate the potential of assimilating LAI through direct insertion in a land surface model." Journal of Hydrology X 9, no. : 100063.
Vegetation plays a fundamental role not only in the energy and carbon cycles but also in the global water balance by controlling surface evapotranspiration (ET). Thus, accurately estimating vegetation-related variables has the potential to improve our understanding and estimation of the dynamic interactions between the water, energy, and carbon cycles. This study aims to assess the extent to which a land surface model (LSM) can be optimized through the assimilation of leaf area index (LAI) observations at the global scale. Two observing system simulation experiments (OSSEs) are performed to evaluate the efficiency of assimilating LAI into an LSM through an ensemble Kalman filter (EnKF) to estimate LAI, ET, canopy-interception evaporation (CIE), canopy water storage (CWS), surface soil moisture (SSM), and terrestrial water storage (TWS). Results show that the LAI data assimilation framework not only effectively reduces errors in LAI model simulations but also improves all the modeled water flux and storage variables considered in this study (ET, CIE, CWS, SSM, and TWS), even when the forcing precipitation is strongly positively biased (extremely wet conditions). However, it tends to worsen some of the modeled water-related variables (SSM and TWS) when the forcing precipitation is affected by a dry bias. This is attributed to the fact that the amount of water in the LSM is conservative, and the LAI assimilation introduces more vegetation, which requires more water than what is available within the soil.
Xinxuan Zhang; Viviana Maggioni; Azbina Rahman; Paul Houser; Yuan Xue; Timothy Sauer; Sujay Kumar; David Mocko. The influence of assimilating leaf area index in a land surface model on global water fluxes and storages. Hydrology and Earth System Sciences 2020, 24, 3775 -3788.
AMA StyleXinxuan Zhang, Viviana Maggioni, Azbina Rahman, Paul Houser, Yuan Xue, Timothy Sauer, Sujay Kumar, David Mocko. The influence of assimilating leaf area index in a land surface model on global water fluxes and storages. Hydrology and Earth System Sciences. 2020; 24 (7):3775-3788.
Chicago/Turabian StyleXinxuan Zhang; Viviana Maggioni; Azbina Rahman; Paul Houser; Yuan Xue; Timothy Sauer; Sujay Kumar; David Mocko. 2020. "The influence of assimilating leaf area index in a land surface model on global water fluxes and storages." Hydrology and Earth System Sciences 24, no. 7: 3775-3788.
The NASA L-Band Soil Moisture Active Passive (SMAP) satellite mission launched in 2015 has produced soil moisture and freeze thaw (FT) products at a global scale. While the use of L-band (1.41 GHz) passive microwave radiometry (P-MW) has proven useful in detecting changes in the surface FT state, these classifications have not been comprehensively assessed against similar existing FT products, such as the global FT record from the Special Sensor Microwave/Imager (SSM/I, Ka-band, 37.0 GHz) as part of the FT Earth System Data Record (FT-ESDR). In order to fill in this gap, this study investigates regions in which FT classifications diverge and identifies potential sources of classification variability. The SMAP and SSM/I FT records are compared over an extended period covering multiple seasonal cycles from April 2015 through December 2017. The spatially and temporally varying relationship between these products is examined in relation to climate (Köppen-Geiger climate classes and air temperature), MODIS (MoDerate Resolution Imaging Spectrometer) land cover, and topography (using Global Multi-resolution Terrain Elevation Data). SMAP and SSM/I FT product agreement proportion (Ap) was corrected for seasonality and then separated by land cover classes and compared to the global Ap mean. The agreement between these products vary most notably during freeze and thaw onset and in areas near abundant surface water, snow and ice, and wetlands. Relative to other vegetation types, reduced agreement between FT products is also observed over grasslands, sparsely vegetated lands, as well as mixed and evergreen forests. Distinct seasonal differences in FT classification agreement were also detected between products over cold arid regions and between continental and temperate classes. Similarly, as topographic complexity increases, a decreasing trend in agreement between L- and Ka-band FT products is observed. While reiterating challenges in FT classifications identified by prior studies, this work also contributes new insights by providing detailed geospatial and seasonal analyses into the factors contributing to FT product divergence.
Jeremy Johnston; Viviana Maggioni; Paul Houser. Comparing global passive microwave freeze/thaw records: Investigating differences between Ka- and L-band products. Remote Sensing of Environment 2020, 247, 111936 .
AMA StyleJeremy Johnston, Viviana Maggioni, Paul Houser. Comparing global passive microwave freeze/thaw records: Investigating differences between Ka- and L-band products. Remote Sensing of Environment. 2020; 247 ():111936.
Chicago/Turabian StyleJeremy Johnston; Viviana Maggioni; Paul Houser. 2020. "Comparing global passive microwave freeze/thaw records: Investigating differences between Ka- and L-band products." Remote Sensing of Environment 247, no. : 111936.
Rain gauges are unevenly spaced around the world with extremely low gauge density over developing countries. For instance, in some regions in Africa the gauge density is often less than one station per 10 000 km2. The availability of rainfall data provided by gauges is also not always guaranteed in near real time or with a timeliness suited for agricultural and water resource management applications, as gauges are also subject to malfunctions and regulations imposed by national authorities. A potential alternative is satellite-based rainfall estimates, yet comparisons with in situ data suggest they are often not optimal. In this study, we developed a short-latency (i.e. 2–3 d) rainfall product derived from the combination of the Integrated Multi-Satellite Retrievals for GPM (Global Precipitation Measurement) Early Run (IMERG-ER) with multiple-satellite soil-moisture-based rainfall products derived from ASCAT (Advanced Scatterometer), SMOS (Soil Moisture and Ocean Salinity) and SMAP (Soil Moisture Active and Passive) L3 (Level 3) satellite soil moisture (SM) retrievals. We tested the performance of this product over four regions characterized by high-quality ground-based rainfall datasets (India, the conterminous United States, Australia and Europe) and over data-scarce regions in Africa and South America by using triple-collocation (TC) analysis. We found that the integration of satellite SM observations with in situ rainfall observations is very beneficial with improvements of IMERG-ER up to 20 % and 40 % in terms of correlation and error, respectively, and a generalized enhancement in terms of categorical scores with the integrated product often outperforming reanalysis and ground-based long-latency datasets. We also found a relevant overestimation of the rainfall variability of GPM-based products (up to twice the reference value), which was significantly reduced after the integration with satellite soil-moisture-based rainfall estimates. Given the importance of a reliable and readily available rainfall product for water resource management and agricultural applications over data-scarce regions, the developed product can provide a valuable and unique source of rainfall information for these regions.
Christian Massari; Luca Brocca; Thierry Pellarin; Gab Abramowitz; Paolo Filippucci; Luca Ciabatta; Viviana Maggioni; Yann Kerr; Diego Fernandez Prieto. A daily 25 km short-latency rainfall product for data-scarce regions based on the integration of the Global Precipitation Measurement mission rainfall and multiple-satellite soil moisture products. Hydrology and Earth System Sciences 2020, 24, 2687 -2710.
AMA StyleChristian Massari, Luca Brocca, Thierry Pellarin, Gab Abramowitz, Paolo Filippucci, Luca Ciabatta, Viviana Maggioni, Yann Kerr, Diego Fernandez Prieto. A daily 25 km short-latency rainfall product for data-scarce regions based on the integration of the Global Precipitation Measurement mission rainfall and multiple-satellite soil moisture products. Hydrology and Earth System Sciences. 2020; 24 (5):2687-2710.
Chicago/Turabian StyleChristian Massari; Luca Brocca; Thierry Pellarin; Gab Abramowitz; Paolo Filippucci; Luca Ciabatta; Viviana Maggioni; Yann Kerr; Diego Fernandez Prieto. 2020. "A daily 25 km short-latency rainfall product for data-scarce regions based on the integration of the Global Precipitation Measurement mission rainfall and multiple-satellite soil moisture products." Hydrology and Earth System Sciences 24, no. 5: 2687-2710.
Yiwen Mei; Viviana Maggioni; Paul R Houser; Yuan Xue; Tasnuva Rouf. A Nonparametric Statistical Technique for Spatial Downscaling of Precipitation over High Mountain Asia. 2020, 1 .
AMA StyleYiwen Mei, Viviana Maggioni, Paul R Houser, Yuan Xue, Tasnuva Rouf. A Nonparametric Statistical Technique for Spatial Downscaling of Precipitation over High Mountain Asia. . 2020; ():1.
Chicago/Turabian StyleYiwen Mei; Viviana Maggioni; Paul R Houser; Yuan Xue; Tasnuva Rouf. 2020. "A Nonparametric Statistical Technique for Spatial Downscaling of Precipitation over High Mountain Asia." , no. : 1.
The utilization of satellite observations in the estimation of global precipitation is now well established. However, quantifying the errors and uncertainties associated with such estimates is very much in its infancy. While many validation studies have been undertaken, these tend to provide case-specific or longer-term/large area measures of the performance of the precipitation products: statistical performance has largely taken precedence over an assessment of errors and uncertainties within such products. As the requirements for finer spatial and temporal resolutions increase, the assumptions made on the bulk large area/long time-frame products are no longer appropriate: careful assessments of the apportionment of the errors and uncertainties within the precipitation products needs to be made.
The premise of this study is that to truly understand the errors and uncertainties in the final precipitation product it is essential to quantify these within the elements that make up each individual satellite sensor and precipitation retrieval scheme or algorithm. Thus, we start with two fundamental categories: the observation capability of the sensor and the ability of the retrieval scheme. Each sensor provides different observations resulting from the engineering aspects of the sensor itself through to the sampling regime once the sensor is taking measurements: the observation capability is fixed and will be the same for all the subsequent retrieval schemes. The retrieval schemes themselves have a number of assumptions, both in terms of what the sensor actually observes and in the observation-to-rainfall relationships. While many of the errors and uncertainties associated with these assumptions cannot be easily quantified, the relative magnitude of each can be assessed. Initial results are presented here that quantify the effects of the spatial and temporal sampling of sensors, together with the impact of channel selection upon the final products. These results provide an insight into ability of such techniques to retrieve precipitation from the local to global scales, and how such techniques may be improved in the future.
Chris Kidd; Viviana Maggioni. Quantifying errors and uncertainties in satellite precipitation estimates. 2020, 1 .
AMA StyleChris Kidd, Viviana Maggioni. Quantifying errors and uncertainties in satellite precipitation estimates. . 2020; ():1.
Chicago/Turabian StyleChris Kidd; Viviana Maggioni. 2020. "Quantifying errors and uncertainties in satellite precipitation estimates." , no. : 1.
With more satellite and model precipitation data becoming available, new analytical methods are needed that can take advantage of emerging data patterns to make well informed predictions in many hydrological applications. We propose a new strategy where we extract precipitation variability patterns and use correlation map to build the resulting density map that serves as an input to centroidal Voronoi tessellation construction that optimizes placement of precipitation gauges. We provide results of numerical experiments based on the data from the Alto-Adige region in Northern Italy and Oklahoma and compare them against actual gauge locations. This method provides an automated way for choosing new gauge locations and can be generalized to include physical constraints and to tackle other types of resource allocation problems.
Zichao (Wendy) Di; Viviana Maggioni; Yiwen Mei; Marilyn Vazquez; Paul Houser; Maria Emelianenko. Centroidal Voronoi tessellation based methods for optimal rain gauge location prediction. Journal of Hydrology 2020, 584, 124651 .
AMA StyleZichao (Wendy) Di, Viviana Maggioni, Yiwen Mei, Marilyn Vazquez, Paul Houser, Maria Emelianenko. Centroidal Voronoi tessellation based methods for optimal rain gauge location prediction. Journal of Hydrology. 2020; 584 ():124651.
Chicago/Turabian StyleZichao (Wendy) Di; Viviana Maggioni; Yiwen Mei; Marilyn Vazquez; Paul Houser; Maria Emelianenko. 2020. "Centroidal Voronoi tessellation based methods for optimal rain gauge location prediction." Journal of Hydrology 584, no. : 124651.
Vegetation plays a fundamental role not only in the energy and carbon cycle, but also the global water balance by controlling surface evapotranspiration. Thus, accurately estimating vegetation-related variables has the potential to improve our understanding and estimation of the dynamic interactions between the water and carbon cycles. This study aims to assess to what extent a land surface model can be optimized through the assimilation of leaf area index (LAI) observations at the global scale. Two observing system simulation experiments (OSSEs) are performed to evaluate the efficiency of assimilating LAI through an Ensemble Kalman Filter (EnKF) to estimate LAI, evapotranspiration (ET), interception evaporation (CIE), canopy water storage (CWS), surface soil moisture (SSM), and terrestrial water storage (TWS). Results show that the LAI data assimilation framework effectively reduces errors in LAI simulations. LAI assimilation also improves the model estimates of all the water flux and storage variables considered in this study (ET, CIE, CWS, SSM, and TWS), even when the forcing precipitation is strongly positively biased (extremely wet condition). However, it tends to worsen some of the model estimated water-related variables (SSM and TWS) when the forcing precipitation is affected by a dry bias. This is attributed to the fact that the amount of water in the land surface model is conservative and the LAI assimilation introduces more vegetation, which requires more water than what available within the soil. Future work should investigate a multi-variate data assimilation system that concurrently merges both LAI and soil moisture (or TWS) observations.
Xinxuan Zhang; Viviana Maggioni; Azbina Rahman; Paul Houser; Yuan Xue; Timothy Sauer; Sujay Kumar; David Mocko. The Influence of Assimilating Leaf Area Index in a Land Surface Model on Global Water Fluxes and Storages. 2019, 2019, 1 -28.
AMA StyleXinxuan Zhang, Viviana Maggioni, Azbina Rahman, Paul Houser, Yuan Xue, Timothy Sauer, Sujay Kumar, David Mocko. The Influence of Assimilating Leaf Area Index in a Land Surface Model on Global Water Fluxes and Storages. . 2019; 2019 ():1-28.
Chicago/Turabian StyleXinxuan Zhang; Viviana Maggioni; Azbina Rahman; Paul Houser; Yuan Xue; Timothy Sauer; Sujay Kumar; David Mocko. 2019. "The Influence of Assimilating Leaf Area Index in a Land Surface Model on Global Water Fluxes and Storages." 2019, no. : 1-28.
The Washington D.C area. This work investigates the potential of using satellite-based precipitation products in a hydrological model to estimate water quality indicators in the Occoquan Watershed, located in the suburban Washington D.C area. Three (3) satellite-based precipitation products based on different retrieval algorithms (the Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis, TMPA 3B42-V7; the Climate Prediction Center’s CMORPH product; and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System, PERSIANN−CCS) are compared to gauge-based records over a 5-year period across the study region. The 3 satellite-based precipitation products and the gauge-based dataset are used as input to the Hydrologic Simulation Program FORTRAN (HSPF) hydrology and water quality model. Each satellite precipitation-forced simulation is compared to the reference model simulation forced with the gauge-based observations, in terms of streamflow and water quality indicators, i.e., stream temperature (TW), total suspended solids (TSS), dissolved oxygen (DO), and biological oxygen demand (BOD). Results indicate that the spatiotemporal variability observed in the satellite-based precipitation products has a quantifiable impact on both modeled streamflow and water quality indicators. All 3 satellite products present moderate agreements with the reference precipitation and simulation; CMORPH presenting the best overall performance followed closely by TMPA, and PERSIANN presenting a comparatively inferior performance in terms of correlation, root-mean-square error and bias for streamflow and water quality indicators, such as TW, TSS, DO and BOD concentrations.
Jennifer Solakian; Viviana Maggioni; Adnan Lodhi; Adil Godrej. Investigating the use of satellite-based precipitation products for monitoring water quality in the Occoquan Watershed. Journal of Hydrology: Regional Studies 2019, 26, 100630 .
AMA StyleJennifer Solakian, Viviana Maggioni, Adnan Lodhi, Adil Godrej. Investigating the use of satellite-based precipitation products for monitoring water quality in the Occoquan Watershed. Journal of Hydrology: Regional Studies. 2019; 26 ():100630.
Chicago/Turabian StyleJennifer Solakian; Viviana Maggioni; Adnan Lodhi; Adil Godrej. 2019. "Investigating the use of satellite-based precipitation products for monitoring water quality in the Occoquan Watershed." Journal of Hydrology: Regional Studies 26, no. : 100630.
Christian Massari; Luca Brocca; Thierry Pellarin; Gab Abramowitz; Paolo Filippucci; Luca Ciabatta; Viviana Maggioni; Yann Kerr; Diego Fernandez Prieto. Supplementary material to "A daily/25 km short-latency rainfall product for data scarce regions based on the integration of the GPM IMERG Early Run with multiple satellite soil moisture products". 2019, 1 .
AMA StyleChristian Massari, Luca Brocca, Thierry Pellarin, Gab Abramowitz, Paolo Filippucci, Luca Ciabatta, Viviana Maggioni, Yann Kerr, Diego Fernandez Prieto. Supplementary material to "A daily/25 km short-latency rainfall product for data scarce regions based on the integration of the GPM IMERG Early Run with multiple satellite soil moisture products". . 2019; ():1.
Chicago/Turabian StyleChristian Massari; Luca Brocca; Thierry Pellarin; Gab Abramowitz; Paolo Filippucci; Luca Ciabatta; Viviana Maggioni; Yann Kerr; Diego Fernandez Prieto. 2019. "Supplementary material to "A daily/25 km short-latency rainfall product for data scarce regions based on the integration of the GPM IMERG Early Run with multiple satellite soil moisture products"." , no. : 1.